论文标题
全球数据协会与3D Grassmannian歧管对象
Global Data Association for SLAM with 3D Grassmannian Manifold Objects
论文作者
论文摘要
与普遍使用的点云图相比，使用LiDAR猛击中的极和平面对象可以提高准确性并降低地图存储要求。但是，由于对全球匹配而没有初始猜测的要求，使用这些地标的位置识别和几何验证是具有挑战性的。现有作品通常仅利用杆或平面地标，将应用限制在受限制的环境集中。我们使用3D线和平面对象同时且以统一的方式提出了一种全局数据关联方法，用于LIDAR扫描中的循环闭合方法。本文的主要新颖性在于在仿射子空间（称为仿生的grassmannian）的歧管上从激光扫描中提取的线和平面物体的代表。使用我们的基于图的数据关联框架匹配线路和平面对应关系，然后以最小二乘的意义注册。与仅一根杆的方法和纯平面方法相比，我们的3D仿生草个方法分别提高71％和325％，以在Kitti数据集的100％精度下以100％的精度循环召回，并且可以提供小于10 cm和1度误差的框架对齐。
Using pole and plane objects in lidar SLAM can increase accuracy and decrease map storage requirements compared to commonly-used point cloud maps. However, place recognition and geometric verification using these landmarks is challenging due to the requirement for global matching without an initial guess. Existing works typically only leverage either pole or plane landmarks, limiting application to a restricted set of environments. We present a global data association method for loop closure in lidar scans using 3D line and plane objects simultaneously and in a unified manner. The main novelty of this paper is in the representation of line and plane objects extracted from lidar scans on the manifold of affine subspaces, known as the affine Grassmannian. Line and plane correspondences are matched using our graph-based data association framework and subsequently registered in the least-squares sense. Compared to pole-only approaches and plane-only approaches, our 3D affine Grassmannian method yields a 71% and 325% increase respectively to loop closure recall at 100% precision on the KITTI dataset and can provide frame alignment with less than 10 cm and 1 deg of error.
